---
title: "Resistance is Not Futile: Rethinking Ethnicity, Tactics, and Outcomes in Civil Conflicts"
author:
  - name: Robert Tanner Bivens
    affiliation: Department of Political Science, Eastern Illnois University
  
  - name: Ches Thurber
    affiliation: Department of Political Science, Northern Illinois University

format:
  pdf:
    keep-tex: true
    cite-method: biblatex
    biblatexoptions:
      - style=apa
      
bibliography: "RethinkingEthnicity.bib"
date: today
linestretch: 2
indent: true
mainfont: "Palatino"
sansfont: "Optima"

abstract-title: "Word Count:"
abstract: "Recent studies have questioned whether nonviolent tactics can be effective for ethnic minorities. However, they often overlook multiethnic coalitions, shifts in campaign composition, and ethnicity's parallel role in armed tactics. This paper re-evaluates the relationship between ethnicity, tactics, and outcomes in civil conflicts. To do so, we introduce new data on Ethnic Groups in Contention (EGC) that offer time-variant measures of the ethnic attributes of campaigns.  We find that the effectiveness of nonviolent tactics for ethnic minorities depends on the point of comparison. Campaigns composed solely of excluded groups succeed less often than those made up entirely of privileged groups. However, minorities have still fared better when using nonviolent as compared to violent tactics. Additional analyses explore ethnic diversity, multiethnic coalitions, hybrid tactics, and alternative measures of success. Taken together, our findings complicate a prevailing assertion that nonviolent tactics are only effective for members of privileged groups."

---

```{r packages, include=FALSE}
library(dplyr)
library(modelsummary)
library(ggeffects)
library(ggplot2)
library(kableExtra)
library(patchwork)

knitr::opts_chunk$set(
   echo = FALSE, message=FALSE, warning=FALSE, cache = FALSE)
```

```{r import, include=FALSE}
NAVCO_EGC_VDEM <- read.csv("EGC_CampYear_20250930.csv")
NAVCO_EGC_VDEM$prim_meth <- factor(NAVCO_EGC_VDEM$prim_meth)
```

**Keywords:** Nonviolent Resistance, Civil War, Ethnic Conflict, Social Movements

-   Corresponding author: Ches Thurber, Department of Political Science, Northern Illinois University, cthurber\@niu.edu.

Accepted for publication at the *Journal of Peace Research.*

# Author Bios

ROBERT TANNER BIVENS, b. 1989, PhD in Political Science (Northern Illinois University, 2024); Instructor, Department of Political Science, Eastern Illinois University (2024-Present); research interests: sub-Saharan African politics, comparative regionalism, non-democratic governance.

CHES THURBER, b. 1982, PhD in International Relations (Tufts University, 2015); Associate Professor, Department of Political Science, Northern Illinois University (2016– ); most recent book: *Between Mao and Gandhi: The Social Roots of Civil Resistance* (Cambridge University Press, 2021).

\newpage

# Introduction

In several real-world cases, movements representing ethnic minorities, from Black South Africans to Nepal's indigenous nationalities, have abandoned nonviolent methods in favor of violence after coming to believe that nonviolent tactics could not work for them [@Thurber2021]. Their decisions align with broader intellectual critiques of nonviolent resistance as ineffective and even dangerous for members of ethnic and racial minorities [@Gelderloos2007; @CobbJr.2015; @Jackson2020].

A recent wave of scholarship has offered empirical support for those critiques. Despite the widely celebrated finding that nonviolent tactics have achieved higher rates of success than violent tactics in overthrowing regimes [@Chenoweth2011; @Gleditsch2022], several studies have shown that success rates are far lower when an ethnic cleavage distinguishes the movement from the regime [@Svensson2011; @Pischedda2020]. One major work even found that for ethnic minorities, nonviolent tactics are no more effective than violence [@Manekin2022, 161].

Yet the research on the question of ethnicity and tactics falls short of a consensus. Chenoweth and Stephan, for example, argue that ethnic diversity should make a campaign more effective [@Chenoweth2011], while Cunningham finds that a greater reliance on nonviolent tactics is associated with a higher likelihood of success among ethnic self-determination movements [@Cunningham2023].

The varied results reflect differences in how the studies conceptualize ethnicity, in the types of goals being examined, and in the benchmarks for comparison being used. Furthermore, nearly all prior studies have been hindered by limited data, forcing them to treat ethnicity as a static and binary dichotomy. This has prevented them from being able to evaluate different dimensions of ethnicity, to consider the effects of inter-ethnic coalitions, or to account for changes in campaign tactics and ethnic composition over time. 

This paper seeks to systematically reevaluate our knowledge of the relationship between ethnicity, tactics, and outcomes in conflict. We theorize the distinct roles of ethnic diversity and power status in shaping the trajectories and outcomes of conflict, considering which of these dynamics are likely to be universal and which are instead conditional upon the tactics employed by the campaign. To test our arguments, we introduce new data on Ethnic Groups in Contention (EGC).[^1] The data links ethnic groups in the Ethnic Power Relations dataset [@Vogt2015] to campaigns from the NAVCO 2.1 dataset [@Chenoweth2022]. We provide time-variant measures of which ethnic groups participated in a given campaign in each year. This allows us to both account for changes over time, as well as to capture more precise concepts of ethnicity, such as how many different groups participated, the size of those groups, their geographic location, and whether those groups were politically included, excluded, or both. We then conduct a series of descriptive and regression analyses of the varied nature of ethnicity in both violent and nonviolent campaigns and how these ethnic attributes of campaigns relate to outcomes.

[^1]: This version of the dataset, which we are calling EGC 2.1, updates a prior version published by Thurber [-@Thurber2018]. It expands upon that dataset by using the more recently published NAVCO 2.1 dataset for units of analysis and by offering yearly measures that are able to capture changes in campaigns' ethnic composition over time.

Our results confirm those of some prior studies, while challenging others. Consistent with prior work, we find that campaigns are more likely to be successful when they include participation from higher status ethnic groups. However, we find that all types of campaigns, even those made up entirely of politically excluded ethnic groups, have experienced greater success when using nonviolent as compared to violent tactics. We argue that this is because nonviolent tactics still confer some of the same advantages for marginalized groups as they do for ethnic majorities, while violent tactics offer little advantage in overcoming the particular challenges marginalized groups face.

We also assess the impact of ethnic diversity, and find that a greater number of participating ethnic groups is associated with higher rates of success, but only when nonviolent tactics are used.
Furthermore, we recognize that past research has also been limited by narrow conceptualizations of "nonviolence" and "success" [@Turner2023; @Cunningham2023]. We therefore explore the use of hybrid tactics as well as alternative thresholds of success in the latter sections of the paper. We find that campaigns made up entirely of minorities are just as likely as those with privileged groups to achieve concessions short of wholesale regime change or secession. We observe no difference in outcomes between campaigns that employ hybrid strategies and those that maintain more strict nonviolent discipline.

As with all observational studies on tactical effectiveness in conflict, our results are vulnerable to the problem of selection bias. In the main text and an appendix, we discuss the most likely sources of endogeneity, run year- and country-level fixed effects models to account for omitted variables that might be correlated with those units, and present sensitivity analyses. These are far from a cure-all for the problem of selection bias, but they allow us to better assess the potential direction and degree of the problem and to contextualize our interpretations accordingly. Most crucially, our findings should be understood as those that emerge from within the set of campaigns that are able to meet the NAVCO dataset's selection criteria of mobilizing at least 1,000 participants.

Given the impact of previous observational findings on ethnicity in civil resistance campaigns to scholarly debates as well as to public discourse, we believe that the more detailed scrutiny of the historical data offered by this paper is needed.

# Ethnicity and the Outcomes of Violent and Nonviolent Campaigns

Underpinning theories of the impact of "ethnicity" on conflict are distinct mechanisms that link different dimensions of ethnicity to specific conflict dynamics. Some theories highlight the advantages of ethnic heterogeneity on building mass resistance and making repression difficult [@Chenoweth2011]. Others emphasize the challenges that come from exclusion from institutions of political power or from small demographic size [@Thurber2018; @Pischedda2020]. And others still focus on social connections or shared identity frames between participants and those on the sidelines [@Svensson2011; @Manekin2022] .

In most cases, prior work has relied on a simplified dichotomy, considering conflicts to either be inherently "ethnic" or "not ethnic" along some prescribed criteria. Pischedda [-@Pischedda2020] focuses on whether or not an ethnic "cleavage" exists between protesters and regime. Thurber [-@Thurber2018] and Manekin and Mitts [-@Manekin2022] assess the demographic size of a campaign's leading ethnic group as well as its power status. Each of these conceptualizations has unique theoretical as well as empirical implications. Conceptually, they all struggle to deal with movements that comprise multiple groups. Operationally, they do not account for changes in the ethnic composition of movements over time. And theoretically, they do not consider whether or not similar obstacles may exist for marginalized groups using armed tactics.

In the section that follows, we untangle these distinct logics, considering how various dimensions of ethnicity are likely to impact the trajectories and outcomes of campaigns across tactics, as well as whether the relative efficacy of nonviolent versus violent tactics is contingent on ethnicity.

## **Ethnic Power Status and Campaign Outcomes**

A major step in the study of ethnic conflict was the move away from demographic measures of ethnicity and toward measures that better account for the power relationships between various ethnic groups and the state [@Cederman2007; @Wimmer2009]. The Ethnic Power Relations project, for example, identified inclusion versus exclusion from political power as a key ethnopolitical dynamic with implications for civil conflict [@Cederman2010; @Vogt2015]. However, much of the focus remained on how ethnic exclusion produces grievances that spark conflict as opposed to how it shapes conflict outcomes.

Thurber [-@Thurber2018] and Pischedda [-@Pischedda2020] both argue that ethnic power relationships underpin dynamics central to the strategic logic of nonviolent conflict. They show that when the participants in a campaign are excluded from holding positions of power in the state, it makes it easier for the regime to engage in repression and less likely that regime elites will defect and join the opposition. In fact, Pischedda finds that an ethnic cleavage in a nonviolent conflict is negatively correlated with both civilian as well as security force defections. Exclusion from security forces is likely to impact dynamics of repression as well. With fewer fears of defections and fewer ties to the opposition, security forces will be more willing to use deadly force against unarmed protesters when those protesters are from a different ethnic group.

Meanwhile, a campaign comprised only of marginalized ethnic groups alleviates the discrimination problem for state forces, making it easier for the regime to infer loyalty on the basis of ethnicity. Only when members of politically privileged ethnic groups are participating in the resistance does the state face the most acute risk of "backfire," in which efforts to repress dissent drive neutrals or loyalists into the arms of the opposition [@Martin2007]. This logic produces the prediction that nonviolent campaigns that are made up of only members of excluded ethnic groups will be less likely to achieve success than those that include members of incumbent groups.

Prior work has not given much attention to the dynamics of campaigns that include both incumbent and excluded ethnic groups. An important exception is Manekin, Mitts, and Zeira [-@Manekin2024], who find that the presence of more privileged "allies" offers advantages to minority-led movements by increasing popular approval, perceptions of nonviolence, and willingness to participate among members of privileged groups. The logic of Thurber [-@Thurber2018] and Pischedda [-@Pischedda2020] would also suggest that the participation of a privileged group should increase the prospects for success by providing social connections to the regime. Furthermore, the presence of members of an incumbent group within the campaign complicates efforts at identifying and targeting dissidents.

However, it is also possible that the mere presence of a marginalized group in a campaign, even when alongside participation from a more powerful group, can trigger negative consequences for the campaign. In the context of the 2011 Syrian uprising, McLauchlin [-@McLauchlin2017], notes that even though members of the dominant Alawite group participated, the large presence of Sunnis in the movement allowed the regime to stoke ethnic fears, undermining the movement's perceived legitimacy and building support for brutal repression.

Given this confluence of competing dynamics, we expect these cases of mixed participation to experience rates of success somewhere in the middle: lower than campaigns made up entirely of members of incumbent groups, but higher than campaigns made up entirely of excluded groups.

The arguments above draw primarily from scholarship focusing on the role of ethnic power status in nonviolent conflicts. But both theory and prior empirical research suggest that minorities engaged in armed insurrection may face similar impediments. While civil war scholarship has generally been more focused on etiology than outcomes, quantitative studies that have analyzed the link between ethnicity and effectiveness have found rebel victory to be substantially less likely when a war is "ethnic" versus "non-ethnic" [@Mason1996; @deRouen2004].

Why is this the case? Some of the same mechanisms cited in the literature on civil resistance likely apply to insurgencies as well. For example, the relationship between ethnicity and defections highlighted in the nonviolent conflict literature has also been noted in civil war scholarship. Armed rebellions by "in-groups" are more likely to splinter state security forces, creating greater opportunity for success, while rebellions by excluded minorities are less likely to challenge the loyalty of the military [@McLauchlin2010]. Meanwhile, the role of ethnicity as a marker to discern loyalty originally comes from the armed conflict literature and is the theoretical rationale behind Mason, Weingarten, and Fett's finding that ethnic rebellions experience lower rates of victory [-@Mason1996, p. 263].

Some ethnic dynamics may impact unarmed tactics to a greater degree than armed ones. We turn to a discussion of these relative differences in the next section. But because marginalized status creates obstacles for excluded ethnic groups both when they use violence as well as nonviolence, we expect the association between ethnic status and success to exist across both tactical repertoires.

> **H1:** *Campaigns involving ethnic groups with higher levels of political power will be more likely to succeed, regardless of tactics used.*

## **Is the nonviolent advantage conditional on ethnicity?**

The previous section advanced the argument that excluded ethnic groups will face lower odds of success whether they employ primarily armed or unarmed methods. But is there still an advantage to using nonviolent tactics for politically excluded groups?

Manekin and Mitts argue that in-groups tend to view any resistance by members out-groups as "violent," thereby negating the advantages of adhering to nonviolent tactics [@Manekin2022, 162]. But even with notable disadvantages as compared to incumbent groups, excluded minorities may still be able to harness many of the benefits of a nonviolent strategy. Ethnic biases may prevent local audiences from viewing minority protest favorably, but an adherence to primarily nonviolent tactics might help such a movement gain international support. Such support was critical, for example, in the end of the Apartheid regime in South Africa.

And while ethnic cleavages are likely to be an impediment to mass mobilization, unarmed tactics might better enable a movement to maximize participation among co-ethnics and make it more likely to attract at least some support across ethnic divides as compared to if dissidents used violent force. Chenoweth and Stephan (2011) highlight both of these dynamics in their analysis of why they believe the first Palestinian intifada was more successful during the years in which it was primarily nonviolent .

Finally, while excluded ethnic groups are likely to face greater repression from the state, they are just as likely (if not more) to face such repression if they took arms. When an excluded group adheres to nonviolent tactics, it increases the cost of that repression to the regime. Even if ethnic divisions limits local sympathy from privileged ethnic groups, international actors may be more likely to intervene [@Cunningham2023]. For example, the Dilli Massacre of 1991 brought international support to the East Timorese movement, leading eventually to a UN-sponsored referendum in 1999 and independence in 2002.

In sum, members of excluded ethnic groups engaged in nonviolent tactics may struggle to mobilize, face greater repression, and more frequently fail to win over elite defections as compared to members of incumbent ethnic groups. But they struggle even more on these fronts when they use violent tactics. Several case study analyses of minority resistance movements in Palestine, South Africa, and East Timor, for example, have concluded that those movements achieved relatively greater success during periods of time in which they used primarily nonviolent tactics [@Chenoweth2011, pp. 138-145; @Barrell1992; @Zunes1999b; @MacLeod2015; @Stephan2008]. This logic produces the following hypothesis.

> **H2:** *Campaigns using nonviolent tactics will be more likely to succeed, regardless of participants' ethnic power status.*

In Table 1, we bring together our theoretical predictions from Hypotheses 1 and 2. We show that within each tactic, we expect groups with higher ethnic status levels to enjoy higher rates of success, but that across the board we expect success rates to be higher when nonviolent tactics are used. We classify the ethnic composition of campaigns into three status categories: "Incumbents" refers to campaigns in which all participating ethnic groups have access to state power, "Excluded" refers to campaigns in which all participating ethnic groups are excluded from power, and "Mixed" refers to campaigns in which at least one included and one excluded group are present.

| **Ethnic Status** | **Nonviolent Tactics** | **Violent Tactics** |
|-------------------|------------------------|---------------------|
| **Incumbents**    | Highest                | Low                 |
| **Mixed**         | Higher                 | Lower               |
| **Challengers**   | High                   | Lowest              |

: Predicted success rates based on ethnic status of participants and tactics.

## **Ethnic Diversity and Campaign Success**

While the first two hypotheses focused on ethnic power status, Chenoweth and Stephan's [-@Chenoweth2011] landmark study highlighted a different dimension of ethnicity: the ethnic *diversity* of a campaign. Greater diversity of participants may allow campaigns to tap into more diffuse social networks and leverage a wider set of tactical skills. Furthermore, a campaign with diverse participants may make selective repression by the state more difficult. They write, "The more diverse the participation in the resistance--in terms of gender, age, religion, ethnicity, ideology, profession, and socioeconomic status--the more difficult it is for the adversary to isolate participants and adopt a repression strategy short of maximum and indiscriminate repression" [@Chenoweth2011, 40].

The argument parallels recent social movement research that highlights the importance of inter-ethnic coalitions [@Dixon2013; @Gawerc2020]. Looking beyond ethnicity, Sirianne Dahlum [-@Dahlum2023] finds that more socially heterogenous civil resistance campaigns are more likely to be successful for similar reasons.

While most discussions focus on either a presence or absence of diversity, we should expect the participation of additional ethnic groups (i.e. three or more) to confer even greater benefits. The greater the number of ethnic groups participating, the greater the potential for mobilization and the thornier the challenge of identification on the basis of ethnicity for the regime.

From these arguments, we draw the hypothesis that, for nonviolent campaigns, a greater number of different ethnic groups in a campaign will increase the likelihood for success:

> **H3:** *Among nonviolent campaigns, those with greater ethnic diversity will be more likely to succeed.*

Armed rebels might also benefit both from being able to recruit from multiple subgroups of the population as well as from limiting the state's ability to isolate insurgents' bases of support. But these benefits are likely to be relatively less important given the diminished centrality of mass participation to the strategic logic of insurgency.

Furthermore, studies of insurgency suggest that ethnic homogeneity can be an asset to armed rebels. As Gubler and Selway [-@Gubler2012, 209] write, sharing a single ethnicity can form the basis for identity-based fervor that can motivate individuals to take on the greater risk of potentially lethal participation. It can make it easier for rebel leaders to create and maintain social control within the organization: for the guerrilla commander trying to coerce high-risk mobilization, the strong "bonding" ties of insular networks may be more beneficial than the "bridging" ties afforded by a more diverse movement [@Thurber2021].

For armed conflicts, it is not clear whether diversity or homogeneity is more advantageous. As such, we do not make a prediction about the relationship between diversity and outcomes in violent conflicts.

## 

# Measuring Ethnicity in Mass Uprisings

Testing the hypotheses articulated above requires detailed data on the role of ethnicity in mass uprisings. To date, only limited data has been available. The Nonviolent and Violent Campaign Outcomes (NAVCO) datasets include a binary measure of ethnic diversity [@Chenoweth2013; @Chenoweth2022]. However, they do not differentiate among campaigns with two or more groups participating, nor do they account for dynamics of political power among ethnic groups.

In their analyses of the impact of ethnic cleavages on campaign outcomes, both Svensson and Lindgren [-@Svensson2011] as well as Pischedda [-@Pischedda2020] use a binary measure of whether or not an ethnic cleavage exists between regime and dissidents. But they do not provide information on exactly who is participating in the campaign. They are not able to account for the number of different groups participating, whether the campaign included participation from both included and excluded ethnic groups, or whether dissidents are framing claims in explicitly ethnic terms. They also code only campaigns using primarily nonviolent tactics. The ACD2EPR dataset [@Wucherpfennig2012], by contrast, only includes armed conflicts.

We therefore collected new data on the ethnic attributes of mass uprisings, identifying exactly which ethnic groups participated in the campaign and whether ethnic claims were made. We use data from Thurber [-@Thurber2018] as a point of departure, but we update it in two key respects. First, we use the more recent 2.1 version of the NAVCO dataset to provide our units of analysis [@Chenoweth2022]. This dataset improves upon the prior version by including campaigns as recent as 2013, adding previously omitted campaigns that meet the scope criteria, and adding additional measures for each campaign. Second, while earlier data only provided a snapshot of the ethnic composition at the onset of a campaign, we provide yearly measures, accounting for over-time change within a campaign. In fact, we find that nearly 10 percent of campaigns experience a change in their ethnic composition.

For each NAVCO 2.1 campaign-year, we identify specific ethnic groups from the Ethnic Power Relations (EPR) 2019 dataset that participated in the campaign [@Vogt2015]. We rely on sources such as the ACD2EPR dataset [@Wucherpfennig2012], the Swarthmore Global Nonviolent Action Database [@Lakey2011], qualitative appendices from NAVCO [@Chenoweth2011], and additional secondary sources to determine which groups participated in a campaign. We rely on whether there are reported accounts in the secondary literature of participation of a given ethnic group in a campaign to make our coding decisions. This approach is consistent with prior data, such as the ACD2EPR dataset.

From the list of participating ethnic groups, we produce more general measures of ethnic attributes of each campaign at the yearly level. To examine dynamics of ethnic power and coalition, we divide campaigns into the three categorical types discussed previously: "Incumbents" include only ethnic groups coded by EPR as having access to power, "Excluded" consist entirely of ethnic groups that are excluded from power, and "Mixed" feature participation by at least one included and and at least one excluded ethnic group.

Our data also makes it possible to analyze other dimensions of ethnicity in conflict that have not featured as prominently in recent studies. To assess campaign diversity, we count the total number of groups participating in the campaign. We also note whether or not any participating ethnic group within a campaign makes an ethnic claim, that is, if they articulate explicit political, economic, or social goals that are specific to that ethnic group. Evidence of an ethnic claim often comes in the name of a campaign organization (e.g. "Tigray People's Liberation Front"), or from publicly stated platforms that reference the rights or grievances of a specific group.

Finally, by identifying the specific ethnic groups participating in a campaign, we can incorporate additional data from the Ethnic Power Relations project. This includes the demographic size of participation groups, as well as geographic settlement patterns. Using GeoEPR data [@Wucherpfennig2011], we assess whether any participating group resides in primarily urban areas, as urban versus rural geography is often considered to offer differing advantages to armed and unarmed tactics. Additional information about our data collection and coding procedures can be found in the Online Appendix.

While the NAVCO and EPR datasets provide a strong foundation for this study, a few notes regarding the particularities of those datasets are in order. The NAVCO dataset examines only cases of "maximalist" campaigns, essentially those seeking wholesale regime-change or secession. We think this fits with our theoretical logic which is centered on the presumption of resistance movements that present an existential challenge to the regime and its sovereign control of the territory of the state. It also only includes campaigns once they reach a threshold of 1,000 participants. We discuss the potential for selection bias created by this threshold later in the paper.

Meanwhile, the EPR dataset focuses on ethnic groups that it deems "politically relevant." In some cases, it includes groups, but assigns them a power status of "Irrelevant." In order to include these cases in our analysis, we attempt to assign them a status using procedures described in detail in our Online Appendix (p. 6). We also run analyses in which we omit these cases to demonstrate that they do not drive our findings (Online Appendix, Table 11, p. 20).

```{r count summary, echo=FALSE, message=FALSE, warning=FALSE}
summarydata <- NAVCO_EGC_VDEM %>% 
  mutate(Tactics=case_when(prim_meth==1~"Nonviolent", TRUE~"Violent"),
         Status3 = factor(Status3, levels = c("Excluded", "Mixed", "Incumbents")))%>%
  select(Tactics, "Number of Groups" = num_groups, "Population Share" = pop_share, "Primary Urban Group" = prim_all_urb, "Any Ethnic Claim" = any_claim, "Status" = Status3)

Table2 <- datasummary_balance(~Tactics , data = summarydata, title = 'Ethnic attributes of NAVCO 2.1 campaign-years.', output = "kableExtra")

Table2 %>%
  kable_styling(latex_options = "scale_down")


  
```

Table 2 presents descriptive statistics of the ethnic composition of campaign-years, disaggregated by the primary tactics used. The average number of ethnic groups participating in a campaign in a given year is 1.6 for nonviolent campaigns and 2.0 for violent ones. The larger average number of groups in violent campaigns runs counter to the idea that nonviolent campaigns attract more diverse participation. Instead, it appears that nonviolent campaigns draw participation from slightly larger ethnic groups on average. The mean share of a country's total population made up of ethnic groups participating in a primarily nonviolent campaign was 0.6 while for violent campaigns was 0.4. Meanwhile, the percentage of campaigns where the primary ethnic group is settled in an urban area is slightly higher among nonviolent as compared to violent campaigns. There is a stark difference across campaign tactics when we examine whether or not any participating groups made ethnic claims. Over two-thirds of violent campaign-years feature ethnic claims, while less than half of nonviolent campaign-years do.

There are also notable differences between violent and nonviolent campaigns when it comes to the power status of participating ethnic groups. As shown in Table 2, 42% of campaign-years in which primarily nonviolent tactics were used involved only Incumbents. Only a quarter (25%) were made up entirely of Excluded groups, while another quarter (24%) were Mixed. By contrast, nearly half of violent campaign-years were comprised entirely of Excluded groups. Incumbents account for about a quarter (26%) of campaign years, while campaigns with groups of Mixed status make up 16%. In sum, nonviolent and violent campaigns are reverse images of each other in terms of the power status of their participating ethnic groups.

# Ethnic Attributes and Campaign Outcomes

The new data allow us to examine relationships between the ethnic attributes of campaigns and their outcomes. We rely on outcome measures from NAVCO 2.1, most specifically their dichotomous coding of whether a campaign achieved "success" in a given year, which is defined as their having obtained their ultimate stated goal of regime change or secession [@Chenoweth2022]. It does not achieve success by this measure if it only gains some concessions from the regime. We will examine lower thresholds of success later in this paper.

Table 3 shows the varying rates of success based on the power status of participating ethnic groups. The results are once again disaggregated by the primary tactics employed by the campaign in a given year, reporting our actual observed success rates in a format that parallels our theoretical predictions from Table 1. And, in fact, the empirical data aligns with those predictions.

For nonviolent campaign-years, we see that both Incumbents and Mixed status campaigns achieved success over three times as often as those featuring participation from only Excluded groups (23% to 7%). For violent campaign-years, the pattern with respect to ethnic composition is similar. Incumbents have succeeded four times as often as those with Excluded status (4% to 1%). Mixed status coalitions fall in the middle at a 2% success rate. But across all levels of ethnic status, annual success rates are much higher when primarily nonviolent tactics are used.

```{r success table, echo=FALSE, message=FALSE, warning=FALSE}
summarydata2 <- NAVCO_EGC_VDEM %>% 
  mutate(Tactics=case_when(prim_meth==1~"Nonviolent", TRUE~"Violent"),
         Status = factor(Status3, levels = c("Excluded", "Mixed", "Incumbents"))) %>%
  select("Success" = success, Tactics, Status)
  
Table3 <- datasummary(Heading("")*Mean*(Status) ~ (Success)*Tactics, data = summarydata2, title = 'Ethnic power status and annual rates of campaign success.', output = "kableExtra")

Table3
```

# Regression Analysis

Moving beyond the descriptive trends, we run regression analyses to allow us to control for temporal dependencies as well as potentially confounding variables. They also allow us to analyze multiple ethnic attributes at the same time.[^2] We can see, for example, whether any relationship between ethnic diversity and outcomes is merely a product of those campaigns being more likely to draw participation from privileged groups.

[^2]: We run models that include only one variable related ethnicity at a time in the Online Appendix (Tables 2 and 3). These models produce similar results.

We run a series of logit models, all with cubic polynomials for the number of years since the beginning of the campaign or the last year of campaign success, and all with standard errors clustered by country. Importantly, we control for the size of the ethnic groups participating in the campaign, measured as the sum share of the total country population. This allows us to analyze the dimensions of diversity and power status as distinct from just demographic size. Our measures also allow us to control for ethnic geography, leveraging Geo-EPR data to determine whether or not the largest participating ethnic group in a campaign resides in urban areas [@Wucherpfennig2011]. While this is admittedly a reductive measure of ethnic geography, its inclusion reduces the risk that findings may be biased by urban versus rural geographic settlement patterns.

We also control for a series of state-level variables, including per capita GDP (logged), population (logged), and a series of VDEM indices measuring overall democracy (Polyarchy), civil society participation, egalitarianism, and repression (physical integrity violations). Larger countries with greater state capacity might be more difficult to upend through any type of conflict. Such countries might also have unique ethnic patterns, such as more different ethnic groups or greater rates of inclusion. Repressive and authoritarian regimes also might be more difficult to defeat and more likely to exclude ethnic minorities. Greater civil society has been previously observed to be associated with higher rates of nonviolent campaign success, and might also encourage mobilization by ethnic minorities [@Pinckney2020]. Finally, controlling for the overall level of ethnic equality in a country, as captured by VDEM's egalitarian index, allows us to ensure that any relationships we observe are tied to the specific ethnic groups participating in the campaign, and not how a regime treats ethnic minorities generally. All of these state-level variables are lagged one year.

We present four different models in Table 4. Model 1 includes only the set of campaign-years in which primarily nonviolent tactics were used. Model 2 includes only the primarily violent campaign-years. Model 3 includes all campaign-years with a variable designating the primary tactics. Model 4 includes all campaign-years with interaction terms between primary tactics and both the number of participating ethnic groups as well as the ethnic power status variables. This sequence of models allows us to clearly see and interpret associations between ethnicity and outcomes within each tactic, as well as to evaluate whether these relationships differ across tactics. Campaigns consisting of entirely Excluded ethnic groups serve as the reference category for power status in all models.

```{r Main regressions, echo=FALSE, message=FALSE, warning=FALSE}

NVCamps <- glm(success~ log(num_groups) + Status3 + pop_share + prim_all_urb  + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + success_spell + I(success_spell^2) + I(success_spell^3), data =NAVCO_EGC_VDEM[NAVCO_EGC_VDEM$prim_meth==1,], family = binomial)

#summary(NVCamps)

ViolCamps <- glm(success~ log(num_groups) + Status3 + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + success_spell + I(success_spell^2) + I(success_spell^3), data =NAVCO_EGC_VDEM[NAVCO_EGC_VDEM$prim_meth==0,], family = binomial)

All <- glm(success~ prim_meth + log(num_groups) + Status3 + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) +  v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + success_spell + I(success_spell^2) + I(success_spell^3), data =NAVCO_EGC_VDEM, family = binomial)

Interactions <- glm(success~prim_meth * log(num_groups) + prim_meth*Status3 + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + success_spell + I(success_spell^2) + I(success_spell^3), data =NAVCO_EGC_VDEM, family = binomial)

```

```{r Main Regression Table, echo=FALSE}

Main <- list(
  "(1) Nonviolent" = NVCamps,
  "(2) Violent" = ViolCamps,
  "(3) All" = All,
  "(4) Interactions" = Interactions)

reference <- data.frame("Coefficients" = "Status: Excluded",
                 "(1) Nonviolent" = "-", "(2) Violent" = "-", "(3) All" = "-", "(4) Interactions" = "-")
blank <- data.frame("Coefficients" = "",
                 "(1) Nonviolent" = "", "(2) Violent" = "", "(3) All" = "", "(4) Interactions" = "")
reference = rbind (reference, blank)

attr(reference, "position") <- c(9,10)

Table4 <- modelsummary(Main, stars = T, vcov = ~loc_cow, gof_map = c("nobs"), coef_map = c("prim_meth1" = "Nonviolent", "num_groups" = "Number of Groups", "log(num_groups)" = "Number of Groups (log)", "Status3Incumbents" = "Status: All Incumbents", "Status3Mixed" = "Status: Mixed" , "prim_meth1:log(num_groups)" = "NV * Groups", "prim_meth1:Status3Incumbents" = "NV * Incumbent", "prim_meth1:Status3Mixed" = "NV * Mixed", "pop_share" = "Population Share", "prim_all_urb" = "Urban Primary Group", "log(e_gdppc)" = "GDPpc"  ,"log(e_pop)" = "Population", "v2x_polyarchy" = "Polyarchy", "v2x_cspart" = "Civil Society",  "v2x_egal" = "Egalitarian Index", "v2x_clphy" = "Physical Integrity Index"), add_rows = reference, title = "Ethnic Composition, Tactical Repertoires, and Campaign Outcomes", notes = "All models include cubed temporal controls as well as errors clustered by country.", output = "kableExtra")

Table4 %>%
  kable_styling(latex_options = "scale_down")
  
```

## 

## **Incumbents enjoy greater success**

The models in Table 4 show that the power status of ethnic groups participating in a campaign is correlated with outcomes across campaign tactics. Among nonviolent campaign-years in Model 1, we see a positive correlation for Incumbents (as compared to Excluded status). The size of the coefficient is large, but so is the uncertainty due to the smaller sample size, and the result is significant only at the p \< 0.1 threshold. Campaigns with groups of Mixed status appear no more likely to achieve success than Excluded groups.[^3]

[^3]: This is one case where the inclusion of the number of participating ethnic groups in the model makes a difference. In both the frequency table (Table 2) and the regressions without the number of participating ethnic groups included (Appendix Table 3), Mixed status campaigns using nonviolent tactics are associated with higher rates of success, similar to Incumbents.

Model 2 shows that Incumbents are also associated with a greater likelihood of success among violent campaigns. In this model, Mixed status campaign-years are also associated with higher levels of success.

Figure 1 uses expected probability plots to make better substantive sense of the findings from Model 4. It shows that the likelihood for success among the status levels of all Excluded, Mixed, and all Incumbents for campaign-years that feature primarily nonviolent versus primarily violent tactics. A campaign comprised entirely of Excluded ethnic groups using primarily violent tactics is expected to succeed in 0.4% of campaign-years, while Incumbents using violent tactics are expected to succeed 1.5% of the time. When using primarily nonviolent tactics, that campaign of only Excluded groups has a 6.5% expected chance of success, while Incumbents have an 11.5% expected chance of success.

We interpret these results as supportive of Hypothesis 1; there is a strong relationship between the power status of participating ethnic groups and the likelihood of success across both violent and nonviolent campaigns. This contrast is especially strong when comparing campaign-years in which all participating ethnic groups are Incumbents to those in which all groups are Excluded. The expected success rate for Mixed status campaigns falls between Incumbents and Excluded for violent tactics, but is indistinguishable from that of Excluded among nonviolent campaign-years.

```{r, Coalition Probability Plot, fig.align = 'center', fig.cap = "Expected probabilities by ethnic power status and tactics (Model 4).", fig.height=4}

Coalition_EVs <- ggeffect(Interactions, terms=c("prim_meth", "Status3")) %>%
  rename(Status = group) %>%
  mutate(Status = factor(Status, levels = c("Incumbents", "Mixed", "Excluded")))
  
Coalition_EVs$Tactics = factor(ifelse(Coalition_EVs$x==1, "Nonviolent", "Violent"), levels = c("Violent", "Nonviolent"))


myColors <- c("#7570B3", "#E6AB02", "#7a9a01","#CC6640", "#002F6C")
#names(myColors) <- c("Equality", "Class","Gender", "Ethnicity")


ggplot(Coalition_EVs, aes(x = Tactics, y = predicted, group = Status)) +
  geom_point(aes(color=Status, shape=Status), position = position_dodge(width = 0.4), size = 3)+
  geom_line(aes(color=Status), position = position_dodge(width = 0.4)) +
  geom_errorbar(aes(color = Status), position = position_dodge(width = 0.4), width = 0, ymin=Coalition_EVs$conf.low, ymax=Coalition_EVs$conf.high) +
  labs(x = "",
         y = "Expected Probability of Success",
       color = "Ethnic Status",
       group = "Ethnic Status",
       shape = "Ethnic Status") +
  ylim(0,0.25) +
  ggtitle("") +
  theme_bw() +
  scale_shape_manual(values = c(15, 19, 17, 21)) + 
  scale_color_manual(values = myColors) 

```

## **A nonviolent advantage at all ethnic status levels**

After examining the effect of ethnic power status across tactics, we now examine the effect of tactics at different ethnic status levels. In both Models 3 and 4, the use of primarily nonviolent tactics is strongly and consistently associated with greater likelihood of success. Figure 1 provides a visually compelling illustration of this finding. While the patterns associated with ethnic power status are statistically significant, they are substantively overshadowed by the fact that for all three ethnic status levels, campaigns employing primarily nonviolent tactics have higher expected likelihoods of success. For example, among campaigns of only Excluded ethnic groups, Model 4 anticipates a probability of success of just 0.4% when primarily violent tactics used, compared to 6.5% when primarily nonviolent tactics are used.

To present this another way, Figure 2 shows the pairwise contrasts of nonviolent as compared to violent tactics, conditional on ethnic power status. The marginal effects are positive and statistically significant irrespective of the power status of participating ethnic groups. Across all power status levels, campaigns that employ primarily nonviolent tactics are associated with expected success rates between 4 and 10 percentage points higher than those that use violent tactics.

```{r, Conditional Effects of Nonviolence,  fig.align = 'center', fig.cap = "Conditional effects of nonviolent tactics by ethnic power status (Model 4).", fig.height=4}


Pairwise <- hypothesis_test(Interactions, terms = c( "Status3", "prim_meth"), vcov = ~loc_cow) %>%
  filter(Status3 == "Incumbents-Incumbents" | Status3 == "Mixed-Mixed" | Status3 == "Excluded-Excluded")%>%
  mutate(Condition = replace(Status3, Status3 == "Incumbents-Incumbents", "Incumbents"),
         Condition = replace(Condition, Status3 == "Mixed-Mixed", "Mixed"),
         Condition = replace(Condition, Status3 == "Excluded-Excluded", "Excluded")) %>%
  select(-Status3)



Pairwise_flip <- Pairwise %>%
  mutate(diff = Contrast - conf.low,
         Contrast = -Contrast,
         conf.high = Contrast + diff,
         conf.low = Contrast - diff,
         Tactics = "Nonviolent",
         Ethnicity = factor(Condition, levels = c("Excluded", "Mixed","Incumbents")))

myColors2 <- c("#7a9a01",  "#E6AB02", "#7570B3", "#CC6640", "#002F6C")

ggplot(Pairwise_flip, aes(Contrast, Tactics, color= Ethnicity)) +
    geom_pointrange(aes(xmin = conf.low, xmax = conf.high, shape = Ethnicity), position = position_dodge(width = 0.4)) +
  geom_vline(xintercept = 0) +
   labs(x = "Percentage Point Difference in Expected Outcome",
         y = "Tactics", color = "Ethnic Status", shape = "Ethnic Status") +
  xlim(-.05,.2) +
    theme_bw() +
  scale_shape_manual(values = c(17, 19, 15), guide = guide_legend(reverse = TRUE)) + 
  scale_color_manual(values = myColors2, guide = guide_legend(reverse = TRUE)) 


```

We interpret this as strong support of hypothesis 2, that the use of primarily nonviolent tactics is associated with higher rates of campaign success at all ethnic power status levels. Even when facing the greater obstacles presented by Excluded power status, the use of primarily nonviolent tactics has historically outperformed the use of violent tactics in terms of achieving the stated political goals of the campaign.

Even if nonviolent tactics are associated with higher rates of success for all ethnic status levels, do they confer a greater advantage for the privileged than the marginalized? A close look at Figure 1 shows that the slope of the line for Incumbents appears steeper than that for the Mixed and Excluded status levels. Indeed, incumbents increase their likelihood of success by 10 percentage points, from 1% to 11% , when using nonviolent as opposed to violent tactics. Campaigns made up entirely of excluded groups increase their likelihood of success by only 6 percentage points, from 0.5% to 6.5%. But which is actually greater is a matter of interpretation. The "smaller" 6 percentage point jump for excluded groups actually represents a 13-fold increase in their likelihood of success. This difference in additive versus multiplicative interpretations is reflected in our regression models. The interaction term in Model 4--a logit model based on "log odds"--is not statistically significant. By contrast, when we run a linear probability model as a robustness check in the Appendix (Table 6), it is positive and significant.

The difference also helps explain one source of variance between our findings and some from a recent study by Manekin and Mitts (2022). However, it cannot account for why we find nonviolent tactics to be positively associated with success among marginalized groups, while they do not. We conduct further comparisons in the Online Appendix (pp. 32-34) and conclude that this discrepancy stems primarily from differences in the underlying data. The updated NAVCO 2.1 dataset includes additional cases of excluded groups achieving success through nonviolent tactics that were not included in previous versions. Additionally, our time-variant approach allows us to more precisely code cases like the Second Defiance Campaign in South Africa and the East Timorese independence movement that changed tactics over time. Our data are able to reflect NAVCO's determinations that these campaigns achieved success during years in which they used primarily nonviolent tactics.

In the Online Appendix (p. 34), we run models intended to closely approximate those used by Manekin and Mitts, but using our data. When we do so, we still find a statistically significant positive effect for nonviolent tactics when used by minorities. This further increases our confidence in the empirical support for Hypothesis 2. It is also consistent with recent findings by Cunningham (2023).

## **Diversity helps in nonviolent campaigns**

Next. we examine the relationship between diversity and outcomes as posited in Hypothesis 3. Model 1 shows the expected positive correlation between the (logged) number of groups participating and the likelihood of success among nonviolent campaign-years.[^4] Interestingly, in Model 2, we see that no such relationship exists within violent campaign-years. As a result, Model 4 shows a positive and significant interaction term between tactics and the (log) number of ethnic groups.

[^4]: We take the log to capture potential diminishing returns to diversity. We expect the difference between 1 and 2 groups participating to matter more than the difference between 4 and 5.

To better illustrate the findings in meaningful terms, Figure 3 presents the predicted probabilities of campaign success from Model 4, varying both the number of participating groups and primary tactics. We see that for nonviolent campaigns, the expected likelihood of success increases from just 4% when only 1 group participates, to 8% when 2 groups are participating, to 11% when 5 groups participate. By contrast, the trend among violent campaigns is nearly flat, with a predicted probability of success of less than 1% at all numbers of groups.

```{r, Group Probability Plot, fig.align = 'center', fig.cap = "Expected probabilities by number of groups and primary tactics (Model 4).", fig.height=3.5}


Groups_EVs <- ggeffect(Interactions, terms=c("num_groups [1:10]", "prim_meth")) 

Groups_EVs$Tactics = factor(ifelse(Groups_EVs$group==1, "Nonviolent", "Violent"), levels = c("Violent", "Nonviolent"))


# Robust errors not working...
#, vcov.fun = "vcovCR", vcov.type = "CR0", 
  #vcov.args = list(cluster = NAVCO_EGC_VDEM$id)

myColors3 <- c("#FF0000", "#002F6C")

ggplot(Groups_EVs, aes(x = x, y = predicted, group = Tactics)) +
  geom_line(aes(linetype = Tactics , color = Tactics)) + 
  geom_ribbon(aes(ymin=Groups_EVs$conf.low, ymax=Groups_EVs$conf.high), alpha = 0.1) +
  labs(x = "Number of Groups",
         y = "Expected Probability of Success",
       color = "Primary Tactics",
       group = "Primary Tactics",
       linetype = "Primary Tactics") +
  scale_x_continuous(limits = c(1, 9), breaks = c(1:9)) +
  ggtitle("") +
  theme_bw() + 
   theme(panel.grid.minor = element_blank()) +
  scale_linetype(guide = guide_legend(reverse = TRUE)) +
  scale_color_manual(values = myColors3, guide = guide_legend(reverse = TRUE)) 

```

# Robustness and Sensitivity of the Findings
## Alternative Measures

We further assess the robustness of the findings with additional measures, models, and analyses. They are presented in full form in an Online Appendix and discussed briefly here. First, we examine alternative measures of ethnicity that can be drawn from our dataset. We use a binary measure of diversity (for any campaign with more than one ethnic group). We also use a binary measure of ethnic power status, coding whether or not the ethnic group constituting a plurality of the participants in a given campaign year is an Incumbent or Excluded group. This measure more closely approximates measures used in prior studies. It allows us to assess whether our findings are contingent on our unique coding of "Mixed" status as a distinct category. We use a binary measure of demographic majority as an alternative measure of group size. This also reflects a measure similar to one used in previous research [@Manekin2022]. Finally, we code whether or not a campaign makes explicit ethnic claims, regardless of its ethnic composition, and assess this measure as yet another dimension of ethnicity in conflict. While ethnic claims are highly correlated with the ethnic composition of a movement, campaign actors may make strategic decisions about whether to frame their claims in identity-based or more universal framings [@McLauchlin2017].

Figure 4 presents a panel of expected probability plots for each of these alternative measures. They show patterns consistent with our main findings. In the Online Appendix, we also run models where we exclude all cases with groups that EPR codes as "irrelevant." Again, the results are nearly identical. Thus, while a main point of this paper has been to introduce more complex ways of measuring ethnic dynamics within conflicts, our findings appear to be robust to alternative measures of ethnicity, including those used in prior studies.

```{r, Alternative Measures}

myColors3 <- c( "#7570B3","#7a9a01")
# Diversity (Binary)

#NAVCO_EGC_VDEM$Diverse <- factor(ifelse(NAVCO_EGC_VDEM$num_groups >1, 1, 0))

InterDiv <- glm(success~prim_meth * Diverse + prim_meth*Status3 + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + success_spell + I(success_spell^2) + I(success_spell^3), data =NAVCO_EGC_VDEM, family = binomial)

Diverse_EVs <- ggeffect(InterDiv, terms=c("prim_meth", "Diverse")) %>%
  mutate(Ethnicity = ifelse(group == "1", "Diverse" , "Not Diverse"),
         Dimension = "Diversity",
         Tactics = factor(ifelse(x==1, "Nonviolent", "Violent"), levels = c("Violent", "Nonviolent")))%>%
  filter(!is.na(x))

Diversity_plot <- ggplot(Diverse_EVs, aes(x = Tactics, y = predicted, color = Ethnicity, group = Ethnicity)) +
  geom_point(aes(color=Ethnicity, shape = Ethnicity), position = position_dodge(width = 0.4))+
  geom_line(aes(color=Ethnicity), position = position_dodge(width = 0.4)) +
  geom_errorbar(aes(color = Ethnicity), position = position_dodge(width = 0.4), width = 0, ymin=Diverse_EVs$conf.low, ymax=Diverse_EVs$conf.high) +
  labs(x = "",
         y = "Success",
       color = "",
       group = "",
       shape = "") +
  ylim(0,0.25) +
  facet_wrap(~Dimension) +
  ggtitle("") +
  theme_bw() +
  scale_shape_manual(values = c(15,17)) + 
  scale_color_manual(values = myColors3)

# Power Status (Binary, 1 = Challenger Coalition)


InterPrimEgip <- glm(success~prim_meth * log(num_groups) + prim_meth*prim_egip + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + success_spell + I(success_spell^2) + I(success_spell^3), data =NAVCO_EGC_VDEM[NAVCO_EGC_VDEM$any_irrs ==0,], family = binomial)


PrimEgip_EVs <- ggeffect(InterPrimEgip, terms=c("prim_meth", "prim_egip")) %>%
  mutate(Ethnicity = factor(ifelse(group == "1", "Incumbent" , "Excluded"), levels = c("Incumbent","Excluded")),
         Dimension = "Plurality Status",
         Tactics = factor(ifelse(x==1, "Nonviolent", "Violent"), levels = c("Violent", "Nonviolent")))%>%
  filter(!is.na(x))

PrimEgip_plot <- ggplot(PrimEgip_EVs, aes(x = Tactics, y = predicted, color = Ethnicity, group = Ethnicity)) +
  geom_point(aes(color=Ethnicity, shape = Ethnicity), position = position_dodge(width = 0.4))+
  geom_line(aes(color=Ethnicity), position = position_dodge(width = 0.4)) +
  geom_errorbar(aes(color = Ethnicity), position = position_dodge(width = 0.4), width = 0, ymin=PrimEgip_EVs$conf.low, ymax=PrimEgip_EVs$conf.high) +
  labs(x = "",
         y = "Success",
       color = "",
       group = "",
       shape = "") +
  ylim(0,0.25) +
  facet_wrap(~Dimension) +
  ggtitle("") +
  theme_bw() +
  scale_shape_manual(values = c(15,17)) + 
  scale_color_manual(values = myColors3)

# Demography (1 = demographic majority)

#NAVCO_EGC_VDEM$Majority <- factor(ifelse(NAVCO_EGC_VDEM$pop_share > .5, 1, 0))

InterMaj <- glm(success~prim_meth * log(num_groups) + prim_meth*Majority + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + success_spell + I(success_spell^2) + I(success_spell^3), data = NAVCO_EGC_VDEM, family = binomial)

Majority_EVs <- ggeffect(InterMaj, terms=c("prim_meth", "Majority")) %>%
  mutate(Ethnicity = ifelse(group == "1", "Majority" , "Minority"),
         Dimension = "Demography",
         Tactics = factor(ifelse(x==1, "Nonviolent", "Violent"), levels = c("Violent", "Nonviolent")))%>%
  filter(!is.na(x))

Majority_plot <- ggplot(Majority_EVs, aes(x = Tactics, y = predicted, color = Ethnicity, group = Ethnicity)) +
  geom_point(aes(color=Ethnicity, shape = Ethnicity), position = position_dodge(width = 0.4))+
  geom_line(aes(color=Ethnicity), position = position_dodge(width = 0.4)) +
  geom_errorbar(aes(color = Ethnicity), position = position_dodge(width = 0.4), width = 0, ymin=Majority_EVs$conf.low, ymax=Majority_EVs$conf.high) +
  labs(x = "",
         y = "Success",
       color = "",
       group = "",
       shape = "") +
  ylim(0,0.25) +
  facet_wrap(~Dimension) +
  ggtitle("") +
  theme_bw() +
  scale_shape_manual(values = c(15,17)) + 
  scale_color_manual(values = myColors3)

# Ethnic Claims

InterClaim <- glm(success~prim_meth * log(num_groups) + prim_meth*any_claim + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + success_spell + I(success_spell^2) + I(success_spell^3), data =NAVCO_EGC_VDEM, family = binomial)

Claim_EVs <- ggeffect(InterClaim, terms=c("prim_meth", "any_claim")) %>%
  mutate(Ethnicity = factor(ifelse(group == "1", "Ethnic Claim" , "No Claim"),levels = c("No Claim", "Ethnic Claim")),
         Dimension = "Ethnic Claim",
         Tactics = factor(ifelse(x==1, "Nonviolent", "Violent"), levels = c("Violent", "Nonviolent")))%>%
  filter(!is.na(x))


Claim_plot <- ggplot(Claim_EVs, aes(x = Tactics, y = predicted, color = Ethnicity, group = Ethnicity)) +
  geom_point(aes(color=Ethnicity, shape = Ethnicity), position = position_dodge(width = 0.4))+
  geom_line(aes(color=Ethnicity), position = position_dodge(width = 0.4)) +
  geom_errorbar(aes(color = Ethnicity), position = position_dodge(width = 0.4), width = 0, ymin=Claim_EVs$conf.low, ymax=Claim_EVs$conf.high) +
  labs(x = "",
         y = "Success",
       color = "",
       group = "",
       shape = "") +
  ylim(0,0.25) +
  facet_wrap(~Dimension) +
  ggtitle("") +
  theme_bw() +
  scale_shape_manual(values = c(15,17)) + 
  scale_color_manual(values = myColors3)


```

```{r, Alternative Measures Plots, figures-side, fig.cap = "Probability plots using alternative binary measures of ethnic dimensions.", fig.width = "6in", out.width = "6in" , fig.height = "4in", out.height = "6in"}

(Diversity_plot + PrimEgip_plot) / (Majority_plot + Claim_plot)



```

## Alternative Modeling Choices

After exploring different measures of ethnicity, we also test the robustness of our findings to different model specifications. We run models on the subset of campaigns seeking regime change to ensure our findings are not the result of secessionist versus center-seeking goals. We also run models with additional control variables for simultaneous campaigns of different tactics, past participation in violent or nonviolent campaigns, international support, and transnational ethnic kin. Our results do not change when these variables are included.

Finally, we run year-level, country-level, and two-way fixed effects to account for potential unobserved confounding variables that could be correlated with these units. This is a powerful robustness test, as it effectively controls for a wide array of potential unmeasured confounding variables associated with country and time, such as colonial history, past conflict, ethnic demographics, and state capacity. Once again, the results are nearly identical to those of the main models presented in Table 4.

## Selection bias

The fixed effects models lead us to the next concern, which is the possibility of endogeneity bias in an observational study. While the results of the fixed effects models help us rule out omitted variables that are correlated with time and country, we still worry about the potential for unmeasured factors that condition movements' prospects for success and groups' willingness to participate in a resistance campaign that uses either primarily violent or primarily nonviolent tactics.[^5]

[^5]: To even be included in the NAVCO dataset, a campaign needs to mobilize at least 1,000 participants, which is itself a metric of success. And that threshold might be more or less difficult to achieve depending upon tactics used. We consider this as part of the types of "selection effects" to be examined in this section.

For the power status measures, members of lower status groups may be less likely to believe that resistance would be successful and to choose not to initiate or participate in a resistance campaign. Those that do anyway could possibly be doing so because they have some other reason to believe that they are likely to be successful that we are unable to measure in our models. To put it differently, our set of observed Excluded cases could be biased in the direction of those that are more likely to succeed. If this is the case, it would mean that our measured association between power status and success is an *underestimate*. In the absence of selection bias, our results would actually be stronger. We conduct sensitivity analyses for the power status variables in the appendix as well. It would not take that powerful of an omitted confounding variable to undo our findings. In fact, the power status variables already fall just below traditional thresholds of significance even in our main model for nonviolent campaign-years. But again, we think that in the absence of selection bias, our findings pertaining to power status would likely be even stronger.

It is possible that there is both a selection effect for power status as described above, and that the degree of the selection effect could vary across violent and nonviolent tactics. If members of Excluded groups were more likely to be deterred from engaging in nonviolent resistance, but were nevertheless willing to attempt violent resistance, it would complicate our ability to interpret the relative association between tactics and success across ethnic power levels. The low share of nonviolent campaign-years coded as Excluded as compared to violent campaign-years is suggestive that this might be the case [see also @Thurber2018]. Once again, we turn to sensitivity analyses to assess the vulnerability of our findings to this issue. Because of the difficulty in interpreting a sensitivity analysis with interaction terms, we instead run separate models for the set of campaigns falling into each of the levels of our ethnic status power variable. Most relevant to our hypotheses, we find that among campaigns made up of only Excluded groups, unobserved confounding variables would have to explain more than 11% of the residual variance in both treatment and outcome to make the correlation between nonviolent tactics and success no longer statistically significant. Compared to the strongest confounding variable included in the model, country population, unmeasured confounders would have to be more than five times as strong before the results were no longer statistically significant.

In the case of diversity, we believe the biggest risk may be that when bystanders see a movement that they perceive as likely to be successful, they are more likely to participate, thus increasing the number of different groups participating. To see if this might be the case, we run models in which we control for campaign size (Online Appendix, p. 22). When we do so, the relationship between number of groups and success is no longer significant even within nonviolent campaigns. But the causal ordering here is not clear. Indeed, part of the logic of our diversity hypothesis is that greater diversity expands the reach of social networks available to a campaign making greater mobilization possible. If so, controlling for campaign size would introduce post-treatment bias. Further research might wish to more precisely unpack the timing of campaigns diversity, size, and outcomes.

# Expanding Concepts of "Success" and "Nonviolence"

The primary goal of this paper has been to offer a more thorough analysis of the role of ethnicity in conflict. But the literature on the outcomes of violent and nonviolent campaigns has frequently been criticized for oversimplifying both the dichotomy between "violence" and "nonviolence" as well as between "success" and "failure" [@Turner2023]. These simplifications may be especially problematic for the inquiry into the role of ethnicity.

For example, fearing greater repression, minority groups may be more likely to engage in "hybrid" strategies in which protesting masses are defended by armed flanks, or in which such flanks engage in violent offensive attacks for greater coercive leverage. The uMkhonto we Sizwe within the anti-Apartheid movement in South Africa is perhaps the most prominent example. Meanwhile, even when marginalized groups are not able to achieve their full goal, obtaining even limited concessions can be very consequential [@Cunningham2023]. Understanding if and when groups are able to achieve smaller steps toward progress is an important outcome that has previously been overlooked.

In this section, we analyze the relationship between ethnicity, strategy, and campaign outcomes using more precise measures of both strategy and outcome. First, we use NAVCO 2.1's measure of "violent flanks" to subdivide the category of nonviolent campaigns into "hybrids" that included the presence of a flank that used violent tactics, versus those that maintained more complete nonviolent discipline. We run an interaction model that parallels that used in our main analysis (Table 3, Model 4). The results are presented in Figure 5 and in tabular form in the Online Appendix. Figure 5 shows all three pairwise contrasts within the re-coded 3-level tactics variable for each level of ethnic status. It shows that the more strictly nonviolent campaigns and those employing hybrid tactics are largely associated with similar outcomes: both outperform campaigns that use primarily violent tactics, though the confidence intervals are now wider as there are fewer cases within each of the nonviolent factor levels. The results are largely consistent across ethnic coalition types.

Most noteworthy is the fact that we see no evidence of hybrid tactics being advantageous to Excluded ethnic groups. Violent flanks are sometimes argued to be helpful or even necessary for minorities engaging in nonviolent tactics in order to counter violent regime repression. If anything, the empirical record appears to show the opposite: our model estimates lower expected rates of success when Excluded groups incorporate armed flanks as compared to when they use more strictly nonviolent tactics, though the difference is not statistically significant.

```{r Partial Violence, echo=FALSE, message=FALSE, warning=FALSE}
NAVCO_EGC_VDEM$tactics <- factor(NAVCO_EGC_VDEM$tactics, levels = c("Violent", "Hybrid", "Nonviolent"))

Flanks <- glm(success~ tactics * Status3 + tactics* log(num_groups) + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + success_spell + I(success_spell^2) + I(success_spell^3), data =NAVCO_EGC_VDEM, family = binomial)

```

```{r, flanks contrast plot, echo=FALSE, message=FALSE, warning=FALSE, fig.align = 'center', fig.cap = "Disaggregating violent flanks.", fig.height=3.5}

IC <- "Incumbents"
FlanksCoalition_pairwise_IC <- hypothesis_test(Flanks, terms = c("tactics", "Status3 [IC]"), vcov = ~loc_cow)%>%
  mutate(Type = "Incumbents") %>%
  select(-Status3)

MX <- "Mixed"
FlanksCoalition_pairwise_MX <- hypothesis_test(Flanks, terms = c("tactics", "Status3 [MX]"), vcov = ~loc_cow)%>%
  mutate(Type = "Mixed") %>%
  select(-Status3)

EX <- "Excluded"
FlanksCoalition_pairwise_EX <- hypothesis_test(Flanks, terms = c("tactics", "Status3 [EX]"), vcov = ~loc_cow) %>%
  mutate(Type = "Excluded") %>%
  select(-Status3)



Flanks_pairwise <- rbind(FlanksCoalition_pairwise_IC, FlanksCoalition_pairwise_MX, FlanksCoalition_pairwise_EX)

Flanks_pairwise_flip <- Flanks_pairwise %>%
  mutate(diff = Contrast - conf.low,
         Contrast = -Contrast,
         conf.high = Contrast + diff,
         conf.low = Contrast - diff,
         Tactics = replace(tactics, tactics == "Violent-Nonviolent", "Nonviolent (vs. Violent)"),
         Tactics = replace(Tactics, tactics == "Violent-Hybrid", "Hybrid (vs. Violent)"),
         Tactics = replace(Tactics, tactics == "Hybrid-Nonviolent", "Nonviolent (vs. Hybrid)"),
         Tactics = factor(Tactics, levels = c("Hybrid (vs. Violent)", "Nonviolent (vs. Violent)", "Nonviolent (vs. Hybrid)")),
         Ethnicity = factor(Type, levels = c("Excluded", "Mixed", "Incumbents")))

ggplot(Flanks_pairwise_flip, aes(Contrast, Tactics, color=Ethnicity, shape = Ethnicity)) +
    geom_pointrange(aes(xmin = conf.low, xmax = conf.high), position = position_dodge(width = 0.4)) +
  geom_vline(xintercept = 0) +
   labs(x = "Percentage Point Difference in Expected Outcome",
         y = "Pairwise Contrast",
        color = "Ethnic Status",
        shape = "Ethnic Status") +
    theme_bw() +
  scale_shape_manual(values = c(17,19,15),guide = guide_legend(reverse = TRUE)) + 
  scale_color_manual(values = myColors2, guide = guide_legend(reverse = TRUE)) 


```

Next, we use NAVCO 2.1's "progress" variable to assess outcomes at lower thresholds than total victory. We run a series of models on different outcome thresholds from "Any progress" to "Limited concessions" to "Significant concessions." Figure 6 presents the findings, including our original baseline (Model 3) as a point of comparison (see also Online Appendix, Table 18). The results show that as the outcome threshold is lowered, the ethnic attributes of a campaign make less and less of a difference. In fact, neither of the coalition variables is significant at any threshold other than the full success benchmark. While nonviolent strategy is still associated with positive outcomes at all levels, we see the relationship weaken as the threshold of success lowers. Challenger coalitions may face longer odds of achieving total victory, but they are just as likely to achieve more minor concessions as more privileged groups. Such "minor" concessions could be especially meaningful for members of marginalized and discriminated groups [@Cunningham2023].

```{r Progress, echo=FALSE, message=FALSE, warning=FALSE, fig.align = 'center', fig.cap = "Lower thresholds of success.", fig.height=3.5}

Significant <- glm(Significant~prim_meth + log(num_groups) + Status3 + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + progress_spell + I(progress_spell^2) + I(progress_spell^3), data =NAVCO_EGC_VDEM, family = binomial)

Limited <- glm(Limited~prim_meth + log(num_groups) + Status3 + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + progress_spell + I(progress_spell^2) + I(progress_spell^3), data =NAVCO_EGC_VDEM, family = binomial)

Progress <- glm(Any_Progress~prim_meth + log(num_groups) + Status3 + pop_share + prim_all_urb + log(e_gdppc) + log(e_pop) + v2x_polyarchy + v2x_cspart + v2x_egal + v2x_clphy + progress_spell + I(progress_spell^2) + I(progress_spell^3), data =NAVCO_EGC_VDEM, family = binomial)


ProgressPlot <- list(
  "Any Progress" = Progress,
  "Lim. Concessions" = Limited,
  "Sig. Concessions" = Significant,
  "Full Success" = All)

modelplot(ProgressPlot,  coef_map = c("any_claim" = "Ethnic Claim", "Status3Mixed" = "Status: Mixed", "Status3Incumbents" = "Status: Incumbents", "log(num_groups)" = "Groups (log)","prim_meth1" = "Nonviolent"), vcov = ~loc_cow) +
  aes(shape = model, color = model) +
    labs(x = 'Coefficients', 
         y = 'Covariates',
         #title = 'Effect of Civil Resistance Transition on Inclusion',
         #subtitle = 'Limited forms of Success',
         color = "Outcome",
         shape = "Outcome") +
  geom_vline(xintercept=0) +
  theme_bw() +
  scale_shape_manual(values = c(21, 17, 19, 15), name = "Outcome", guide = guide_legend(reverse = TRUE)) + 
  scale_color_manual(values = myColors, guide = guide_legend(reverse = TRUE)) 

```

# Conclusion

Concerns about the obstacles facing ethnic and racial minorities have emerged as one of the most important critiques of the supposed effectiveness of nonviolent tactics. This paper has offered a thorough reassessment of the observational record on the relationship between ethnicity, tactics, and outcomes in civil conflicts.

Our results reaffirm the importance of the power status of participating ethnic groups on campaign outcomes. But they also introduce several important caveats: power status is associated with outcomes in violent as well as nonviolent campaigns, and we observe no significant relationship when examining lower thresholds of success. Most importantly, we show that even for excluded ethnic groups, nonviolent tactics are associated with higher rates of success. These findings should not trivialize the real dangers and challenges facing marginalized groups engaged in dissent, but they offer an important rebuttal to an emerging but over-simplified assertion in public discourse that nonviolent methods "do not work" for ethnic minorities.

These results also have important theoretical implications for understanding the strategic logic of violent and nonviolent conflict. It upends the presumption made in most prior work that the challenges facing ethnic minorities are unique to nonviolent action. We think that those studies accurately identify mechanisms, such as barriers to mass mobilization and lower likelihood of regime defections, that make effective resistance more difficult for politically excluded groups. But armed conflicts may be more reliant on similar mechanisms for success than we often think. As such, these obstacles to mobilization and defection yield lower success rates for minority-led armed campaigns. It might also be the case that commitment problems related to power sharing among ethnic groups in post-conflict orders make negotiated settlements more difficult in both violent and nonviolent conflicts.

Our study also calls attention to the varied dimensions of ethnic identity in conflict and provided new data that make analyses of these varied dimensions possible. Future research might use this data to better understand changes in the ethnic composition of campaigns over time, the relationships among different ethnic groups within a campaign, demographic attributes of participating ethnic groups, and the ethnic geography of conflict.

Indeed, we think that the field might benefit from advancing from the task of measuring the success rates of ethnic minorities in conflict to understanding the unique strategies that minorities might be able to employ to make nonviolent tactics more effective for them. For example, along the lines of a recent study by Manekin, Mitts, and Zeira [-@Manekin2024], experimental work might explore how subjects assess multi-ethnic protest. Or it might seek to manipulate the types of claims made by protesters in addition to their identities. Case studies might explore the puzzle of why minority-led movements struggle to achieve complete success even when they are able to gain significant concessions from the regime. Are the challenges associated with elite defection and civil-military relations a greater barrier than obstacles to early-stage mobilization? Finally, practice-oriented research should move beyond the general "playbook" of civil resistance to identify strategies that might be particularly effective or necessary for campaigns by marginalized ethnic groups.

# Acknowledgements

The authors would like to thank Chris Butler, Cassy Dorff, Jonathan Pinckney, and participants in presentations at the Korbel Research Seminar at Denver University, the Four Corners Conflict Network, and the 2023 International Studies Association Annual Meeting for helpful feedback. We would also like to thank JPR editors and three anonymous reviewers for construtive suggestions that improved this manuscript.

# Funding information

Presentation of an earlier version of this manuscript at the 2023 ISA Annual Meeting in Montreal, Canada was supported, in part, by a grant from the Institute for Human Studies at George Mason University (No. IHS016972).

# Data Availability Statement 

The dataset, codebook, and scripts for the empirical analysis in this article, along with the Online Appendix, are available at https://www.prio.org/jpr/datasets/... as well as at www.chesthurber.com. All analyses were conducted using R.

# References


